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Global and Sliding Window Attention

Overview of Global and Sliding Window Attention Global and Sliding Window Attention is a pattern used in attention-based models to improve efficiency when dealing with long input sequences. It is a modification of the original Transformer model which had non-sparse attention with a self-attention component. The self-attention component had a time and memory complexity of O(n^2) which made it difficult to scale to longer input sequences. Global and Sliding Window Attention overcomes this issue b

Global Average Pooling

Global Average Pooling: A Simplified Way of Feature Extraction Global Average Pooling (GAP) is a popular operation in the field of computer vision designed to replace fully connected layers in classical Convolutional Neural Networks (CNNs). CNNs are a type of deep learning algorithm used for image recognition, classification, and segmentation tasks. Traditionally, the final few layers of a CNN consist of a fully connected (FC) layer followed by a softmax activation function. The FC layer takes

Global Context Block

Global Context Block is an image model block that allows modeling long-range dependencies while still having a lightweight computation. It combines the simplified non-local block and the squeeze-excitation block to create a framework for effective global context modeling. What is Global Context Modeling? Global Context Modeling is a technique used in computer vision to enable machines to recognize objects in images effectively. It involves considering the entire image's context, rather than j

Global Convolutional Network

A Global Convolutional Network, or GCN, is a type of computer algorithm used in image recognition and categorization. It is a building block used to perform two tasks simultaneously: classification and localization. The GCN uses a large kernel to generate semantic score maps, similar to the structure of a Fully Convolutional Network (FCN). How Does a GCN Work? A GCN employs a combination of 1xk + kx1 and kx1 + 1xk convolutions instead of directly using global convolutions or larger kernels. T

Global Coupled Adaptive Number of Shots

gCANS is a cutting-edge quantum algorithm used for stochastic gradient descent. This algorithm is designed to adaptively allocate shots for each gradient component measured at every iteration. With the help of advanced technology, gCANS offers an efficient way to allocate shots based on a criterion that reflects the overall shot cost for the iteration. What Makes gCANS Unique? The unique aspect of gCANS is that it optimizes the use of quantum resources. It does so by adaptively distributing t

Global Local Attention Module

The Global Local Attention Module (GLAM) is a powerful image model block that uses a cutting-edge attention mechanism to enhance image retrieval. GLAM's key feature is its ability to attend both locally and globally to an image's feature maps, allowing for a more thorough understanding of the image's content. The result is a final, weighted feature map that is better suited for image retrieval tasks. Understanding GLAM's Attention Mechanism GLAM's attention mechanism allows it to attend both

Global-Local Attention

Understanding Global-Local Attention and Its Role in ETC Architecture Global-Local Attention is a type of attention mechanism used in the ETC (Encoder-Transformer-Classifier) architecture that helps improve the accuracy of natural language processing tasks. It works by dividing the input data into two separate sequences - the global input and the long input - and then splitting the attention into four different components. This allows the model to selectively focus on different parts of the inp

Global second-order pooling convolutional networks

GSoP-Net Overview: Modeling High-Order Statistics and Gathering Global Information GSoP-Net is a deep neural network architecture that includes a Gsop block with a squeeze module and an excitation module. The GSoP block uses a second-order pooling technique to model high-order statistics and gather global information. This network architecture has been proven to be effective in various computer vision tasks, such as image classification and object detection. The Squeeze Module The squeeze mo

Global Sub-Sampled Attention

Overview of Global Sub-Sampled Attention (GSA) Global Sub-Sampled Attention, or GSA, is a type of local attention mechanism used in the Twins-SVT architecture that summarizes key information for sub-windows and communicates with other sub-windows. This approach is designed to reduce the computational cost needed for attention mechanisms. Local Attention Mechanisms Before diving into GSA, it's important to understand what an attention mechanism is. An attention mechanism is a way for neural n

GloVe Embeddings

What are GloVe Embeddings? GloVe Embeddings are a type of word embedding that represent words as vectors in a high-dimensional space. The vectors capture the meaning of the words by encoding the co-occurrence probability ratio between two words as vector differences. The technique of using word embeddings has revolutionized the field of Natural Language Processing (NLP) in recent years. GloVe is one of the most popular algorithms for generating word embeddings. How are GloVe Embeddings calcu

Glow-TTS

Glow-TTS: The Cutting-Edge TTS System That Delivers Fast, Controllable, and High-Quality Speech Synthesis If you are looking for a state-of-the-art text-to-speech system that delivers high-quality, natural-sounding speech, look no further than Glow-TTS. Glow-TTS is a flow-based generative model for parallel TTS that is designed to produce speech that sounds more lifelike and natural than ever before. This innovative system is able to generate speech without the need for any external alignment

GLOW

GLOW is a powerful generative model that is based on an invertible $1 \times 1$ convolution. This innovative model is built on the foundational work done by NICE and RealNVP. What is GLOW? GLOW is a type of generative model that is used for generating complex data such as images, speech, and music. It operates by learning the underlying distribution of the data and then using this knowledge to generate samples that are similar to the original data. In other words, GLOW is used to create new d

gMLP

gMLP is a new model that has been developed as an alternative to Transformers in the field of Natural Language Processing (NLP). Instead of using self-attention processes, it consists of basic Multi-Layer Perceptron (MLP) layers with gating. The model is organized into a stack of blocks, each defined by a set of equations. The Structure of gMLP The gMLP model is composed of a stack of identical blocks, each of which has the following structure: 1. A linear projection to generate channel pro

Go-Explore

Go-Explore: Effective Exploration in Reinforcement Learning Reinforcement learning is a technique used in artificial intelligence where an agent learns to take actions in an environment to maximize a reward signal. However, one of the main challenges in reinforcement learning is effective exploration. This is where Go-Explore comes in. Go-Explore is a family of algorithms that aims to solve two common problems with exploration in reinforcement learning: Problem #1: Detachment In reinforceme

Goal-Oriented Dialog

Goal-Oriented Dialog Overview Goal-oriented dialog is a type of conversation that is centered around achieving a specific objective or goal. Unlike casual conversations that often have no particular purpose, goal-oriented dialog is structured, intentional, and outcome-driven. It involves two or more participants who engage in a logical, sequential, and purposeful interaction to reach a desired outcome. Goal-oriented dialog can occur in a variety of settings, such as business, education, therap

Goal-Oriented Dialogue Systems

In the era of technological advancement, Goal-Oriented Dialogue Systems (GODS) have become increasingly popular. GODS are systems that can converse in a natural language with a person and are designed to facilitate communication to achieve a pre-defined goal. These systems have now moved from being a luxury to a necessity in various industries such as healthcare, e-commerce, banking, transportation, and more. What are Goal-Oriented Dialogue Systems? A Goal-Oriented Dialogue System (GODS) is a

Good Feature Matching

Good Feature Matching: An Effective Method for Active Map-to-Frame Matchmaking Good feature matching is a technique used in computer vision, which involves matching a set of features between two images. This method is commonly used in robotics, visual navigation, and image recognition applications. The aim of feature matching is to identify the same features in both images and establish a correspondence between them. The process involves identifying key points, or features, in one image and the

GoogLeNet

Overview of GoogLeNet: A Convolutional Neural Network GoogLeNet is a type of convolutional neural network that was developed by a team of researchers at Google. It was introduced in 2014, and it is based on the Inception architecture. This network has been widely used for image recognition and classification tasks, and it has achieved state-of-the-art results on several benchmark datasets. Inception Modules in GoogLeNet The Inception module is a key component of GoogLeNet. It allows the netw

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